With increase in the usage of soft version of images, there has been a need for identifying script and their orientations. Currently, manual checks are performed to categorize the images based on scripts and to correct orientation of the images. However, the manual process can be very time consuming and tedious and may not be cost effective during bulk scanning.
Further, rapid growth in digital libraries has necessitated the need for automated systems for identifying script and their orientations in the images. Furthermore, such automated processing may be required before performing optical character recognition (OCR) analysis.
Existing automated techniques for script and orientation detection of the images are not robust enough to accurately detect the script and orientation and/or are highly computationally intensive.
The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.